
"""simple docstring"""



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A=True       ,   _A="pt"   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = {"""add_prefix_space""": True} if isinstance(_A       ,   _A   ) and not line.startswith(""" """   ) else {}
    _UpperCAmelCase        = padding_side
    return tokenizer(
        [line]       ,   max_length=_A       ,   padding="""max_length""" if pad_to_max_length else None       ,   truncation=_A       ,   return_tensors=_A       ,   add_special_tokens=_A       ,   **_A       ,   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=None       ,   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = input_ids.ne(_A   ).any(dim=0   )
    if attention_mask is None:
        return input_ids[:, keep_column_mask]
    else:
        return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class 		a_    (			_UpperCAmelCase						):







    def __init__(      self      :							Union[str, Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							List[str]="train"  ,       __UpperCamelCase      :							Optional[Any]=None  ,       __UpperCamelCase      :							Any=None  ,       __UpperCamelCase      :							Optional[Any]=None  ,       __UpperCamelCase      :							List[str]=""  ,       )		->str:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase        = Path(__UpperCamelCase  ).joinpath(type_path + """.source"""  )
        _UpperCAmelCase        = Path(__UpperCamelCase  ).joinpath(type_path + """.target"""  )
        _UpperCAmelCase        = self.get_char_lens(self.src_file  )
        _UpperCAmelCase        = max_source_length
        _UpperCAmelCase        = max_target_length
        assert min(self.src_lens  ) > 0, f"""found empty line in {self.src_file}"""
        _UpperCAmelCase        = tokenizer
        _UpperCAmelCase        = prefix
        if n_obs is not None:
            _UpperCAmelCase        = self.src_lens[:n_obs]
        _UpperCAmelCase        = src_lang
        _UpperCAmelCase        = tgt_lang







    def __len__(      self      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        return len(self.src_lens  )







    def __getitem__(      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Tuple  )		->Dict[str, torch.Tensor]:






        '''simple docstring'''






        _UpperCAmelCase        = index + 1  # linecache starts at 1
        _UpperCAmelCase        = self.prefix + linecache.getline(str(self.src_file  )  ,       __UpperCamelCase  ).rstrip("""\n"""  )
        _UpperCAmelCase        = linecache.getline(str(self.tgt_file  )  ,       __UpperCamelCase  ).rstrip("""\n"""  )
        assert source_line, f"""empty source line for index {index}"""
        assert tgt_line, f"""empty tgt line for index {index}"""

        # Need to add eos token manually for T5
        if isinstance(self.tokenizer  ,       __UpperCamelCase  ):
            source_line += self.tokenizer.eos_token
            tgt_line += self.tokenizer.eos_token

        # Pad source and target to the right
        _UpperCAmelCase        = (
            self.tokenizer.question_encoder if isinstance(self.tokenizer  ,       __UpperCamelCase  ) else self.tokenizer
        )
        _UpperCAmelCase        = self.tokenizer.generator if isinstance(self.tokenizer  ,       __UpperCamelCase  ) else self.tokenizer

        _UpperCAmelCase        = encode_line(__UpperCamelCase  ,       __UpperCamelCase  ,       self.max_source_length  ,       """right"""  )
        _UpperCAmelCase        = encode_line(__UpperCamelCase  ,       __UpperCamelCase  ,       self.max_target_length  ,       """right"""  )

        _UpperCAmelCase        = source_inputs["""input_ids"""].squeeze()
        _UpperCAmelCase        = target_inputs["""input_ids"""].squeeze()
        _UpperCAmelCase        = source_inputs["""attention_mask"""].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }







    @staticmethod
    def   _snake_case       (      __UpperCamelCase      :							Optional[int]  )		->Optional[Any]:






        '''simple docstring'''






        return [len(__UpperCamelCase  ) for x in Path(__UpperCamelCase  ).open().readlines()]







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							List[str]  )		->Dict[str, torch.Tensor]:






        '''simple docstring'''






        _UpperCAmelCase        = torch.stack([x["""input_ids"""] for x in batch]  )
        _UpperCAmelCase        = torch.stack([x["""attention_mask"""] for x in batch]  )
        _UpperCAmelCase        = torch.stack([x["""decoder_input_ids"""] for x in batch]  )
        _UpperCAmelCase        = (
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer  ,       __UpperCamelCase  )
            else self.tokenizer.pad_token_id
        )
        _UpperCAmelCase        = (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer  ,       __UpperCamelCase  )
            else self.tokenizer.pad_token_id
        )
        _UpperCAmelCase        = trim_batch(__UpperCamelCase  ,       __UpperCamelCase  )
        _UpperCAmelCase     ,_UpperCAmelCase        = trim_batch(__UpperCamelCase  ,       __UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        _UpperCAmelCase        = {
            """input_ids""": source_ids,
            """attention_mask""": source_mask,
            """decoder_input_ids""": y,
        }
        return batch







a       :						int       		=		getLogger(__name__)


def 			_UpperCamelCase						(    _A   )     ->	Any:





    """simple docstring"""

    return list(itertools.chain.from_iterable(_A   )   )


def 			_UpperCamelCase						(    _A   )     ->	None:





    """simple docstring"""

    _UpperCAmelCase        = get_git_info()
    save_json(_A       ,   os.path.join(_A       ,   """git_log.json"""   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=4       ,   **_A   )     ->	Tuple:





    """simple docstring"""

    with open(_A       ,   """w"""   ) as f:
        json.dump(_A       ,   _A       ,   indent=_A       ,   **_A   )


def 			_UpperCamelCase						(    _A   )     ->	Dict:





    """simple docstring"""

    with open(_A   ) as f:
        return json.load(_A   )


def 			_UpperCamelCase						(    )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = git.Repo(search_parent_directories=_A   )
    _UpperCAmelCase        = {
        """repo_id""": str(_A   ),
        """repo_sha""": str(repo.head.object.hexsha   ),
        """repo_branch""": str(repo.active_branch   ),
        """hostname""": str(socket.gethostname()   ),
    }
    return repo_infos


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	List:





    """simple docstring"""

    return list(map(_A       ,   _A   )   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    with open(_A       ,   """wb"""   ) as f:
        return pickle.dump(_A       ,   _A   )


def 			_UpperCamelCase						(    _A   )     ->	Union[str, Any]:





    """simple docstring"""

    def remove_articles(_A   ):
        return re.sub(R"""\b(a|an|the)\b"""       ,   """ """       ,   _A   )

    def white_space_fix(_A   ):
        return " ".join(text.split()   )

    def remove_punc(_A   ):
        _UpperCAmelCase        = set(string.punctuation   )
        return "".join(ch for ch in text if ch not in exclude   )

    def lower(_A   ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_A   )   )   )   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = normalize_answer(_A   ).split()
    _UpperCAmelCase        = normalize_answer(_A   ).split()
    _UpperCAmelCase        = Counter(_A   ) & Counter(_A   )
    _UpperCAmelCase        = sum(common.values()   )
    if num_same == 0:
        return 0
    _UpperCAmelCase        = 1.0 * num_same / len(_A   )
    _UpperCAmelCase        = 1.0 * num_same / len(_A   )
    _UpperCAmelCase        = (2 * precision * recall) / (precision + recall)
    return fa


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Any:





    """simple docstring"""

    return normalize_answer(_A   ) == normalize_answer(_A   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    assert len(_A   ) == len(_A   )
    _UpperCAmelCase        = 0
    for hypo, pred in zip(_A       ,   _A   ):
        em += exact_match_score(_A       ,   _A   )
    if len(_A   ) > 0:
        em /= len(_A   )
    return {"em": em}


def 			_UpperCamelCase						(    _A   )     ->	Any:





    """simple docstring"""

    return model_prefix.startswith("""rag"""   )







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = {p: p for p in extra_params}
    # T5 models don't have `dropout` param, they have `dropout_rate` instead
    _UpperCAmelCase        = """dropout_rate"""
    for p in extra_params:
        if getattr(_A       ,   _A       ,   _A   ):
            if not hasattr(_A       ,   _A   ) and not hasattr(_A       ,   equivalent_param[p]   ):
                logger.info("""config doesn't have a `{}` attribute""".format(_A   )   )
                delattr(_A       ,   _A   )
                continue
            _UpperCAmelCase        = p if hasattr(_A       ,   _A   ) else equivalent_param[p]
            setattr(_A       ,   _A       ,   getattr(_A       ,   _A   )   )
            delattr(_A       ,   _A   )
    return hparams, config

"""simple docstring"""



def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    if not isinstance(_A       ,   _A   ) or number < 0:
        raise ValueError("""Input must be a non-negative integer"""   )

    _UpperCAmelCase        = 0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
 import doctest

 doctest.testmod()